Probabilistic Forecasting for Day-ahead Electricity Prices, Battery Trading Strategies and the Economic Evaluation of Predictive Accuracy

Probabilistic Forecasting for Day-ahead Electricity Prices, Battery Trading Strategies and the Economic Evaluation of Predictive Accuracy

Swiss Banks and Fintech Firms Explore Innovative Battery Trading Strategies

Section 1 – What happened?

Swiss banks and fintech firms are increasingly exploring innovative battery trading strategies to optimize their energy trading decisions. A recent study published in a leading academic journal highlights the limitations of traditional quantile-based trading strategies (QBTS) used in battery storage arbitrage. The study, which analyzed data from the German electricity market, found that QBTS do not incentivize honest probabilistic forecasting and ignore the intertemporal dependence structure of electricity prices.

Section 2 – Background & Context

The study's findings are significant because they shed light on the pitfalls of ranking forecasting models through battery trading strategies. In recent years, Swiss banks and fintech firms have been investing heavily in energy trading and battery storage solutions to capitalize on the growing demand for renewable energy. However, the accuracy of forecasting models has become a critical factor in determining the economic viability of these investments. The study's authors argue that traditional QBTS are not sufficient to evaluate the performance of forecasting models, as they do not account for the complex intertemporal dependence structure of electricity prices.

Section 3 – Impact on Swiss SMEs & Finance

The study's findings have significant implications for Swiss SMEs and finance firms involved in energy trading and battery storage. The results suggest that traditional QBTS may not accurately reflect the economic value of forecasting models, which could lead to suboptimal investment decisions. To address this issue, the study's authors propose a stochastic programming approach that takes into account the full predictive distribution of electricity prices. This approach could provide a more accurate evaluation of forecasting models and help Swiss firms make more informed investment decisions.

Section 4 – What to Watch

The study's findings have sparked renewed interest in the development of more sophisticated battery trading strategies that account for the complex intertemporal dependence structure of electricity prices. Swiss firms involved in energy trading and battery storage should closely monitor the development of new forecasting models and evaluation methods, as these could provide a competitive edge in the market. Additionally, the study's authors recommend further research into the application of stochastic programming approaches in energy trading and battery storage.

Source

Original Article: Probabilistic Forecasting for Day-ahead Electricity Prices, Battery Trading Strategies and the Economic Evaluation of Predictive Accuracy

Published: April 21, 2026

Author: Simon Hirsch

---

Disclaimer: This article is for informational purposes only and does not constitute financial advice. Consult a licensed financial advisor before making investment decisions.